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Prac2 174 Final

The document outlines a practical assignment to predict total payments for claims using linear regression on two datasets: Swedish auto insurance and salary data. It includes Python code for data loading, model training, prediction, and visualization, along with metrics like Mean Squared Error and R-squared. The program demonstrates how to fit a linear regression model and make predictions based on new input data.

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n0buhosasyk3
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10 views5 pages

Prac2 174 Final

The document outlines a practical assignment to predict total payments for claims using linear regression on two datasets: Swedish auto insurance and salary data. It includes Python code for data loading, model training, prediction, and visualization, along with metrics like Mean Squared Error and R-squared. The program demonstrates how to fit a linear regression model and make predictions based on new input data.

Uploaded by

n0buhosasyk3
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
Available Formats
Download as PDF, TXT or read online on Scribd
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Enrollment no: 202203103510174

Practical-2
Aim-
Write a program to predict total payment for given number of claims on Swedish auto insurance
dataset using linear regression.

❖ With Swedish auto insurance dataset :

Program Code-
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score

df = pd.read_csv("Swedish_Data.csv")

X = df[["X"]].values
Y = df["Y"]

X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.1, random_state=0)

model = LinearRegression()
model.fit(X_train, Y_train)

Y_pred = model.predict(X_test)

mse = mean_squared_error(Y_test, Y_pred)


r2 = r2_score(Y_test, Y_pred)

slope = model.coef_[0]
intercept = model.intercept_

sns.scatterplot(data=df, x="X", y="Y", label='Data Points')


plt.plot(x_range, y_range, color='red', label='Regression Line')

UTU/CGPIT/B.TECH-IT-6 A/Machine Intelligence 18


Enrollment no: 202203103510174

plt.legend()
plt.xlabel('X')
plt.ylabel('Y')
plt.title('Linear Regression Fit')
plt.show()

new_data = [[124]]
predicted_y = model.predict(new_data)
print("Predicted for", new_data[0][0], ":", predicted_y[0])

print(f"Slope: {slope}, Intercept: {intercept}")

print(f"R-squared: {r2}")
print(f"Mean Squared Error: {mse}")

UTU/CGPIT/B.TECH-IT-6 A/Machine Intelligence 19


Enrollment no: 202203103510174

Output-

UTU/CGPIT/B.TECH-IT-6 A/Machine Intelligence 20


Enrollment no: 202203103510174

❖ With other dataset :


Program Code-
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score

df = pd.read_csv("Salary_Dataset.csv")
X = df[["YearsExperience"]].values
Y = df["Salary"]

X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.1, random_state=0)

model = LinearRegression()
model.fit(X_train, Y_train)

Y_pred = model.predict(X_test)

mse = mean_squared_error(Y_test, Y_pred)


r2 = r2_score(Y_test, Y_pred)

m = model.coef_.flatten()
b = model.intercept_.flatten()

plt.plot(X, Y, 'o', label='Data Points') # scatterplot


plt.plot(X, m*X+b, label='Regression Line') # line
plt.legend()
plt.xlabel('X')
plt.ylabel('Y')
plt.title('Linear Regression Fit')
plt.show()

UTU/CGPIT/B.TECH-IT-6 A/Machine Intelligence 21


Enrollment no: 202203103510174

new_data = [[108.55]]
predicted_y = model.predict(new_data)

print("Predicted for",new_data[0][0], ":", predicted_y[0])

print(f"Slope: {m[0]}, Intercept: {b[0]}")


print(f"Mean Squared Error: {mse}")
print(f"R-squared: {r2}")

Output-

UTU/CGPIT/B.TECH-IT-6 A/Machine Intelligence 22

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